Magnetic resonance imaging (MRI) tractography is the mapping of neural fiber architecture based on diffusion-sensitive MRI of tissue anisotropy. To be of general value as an investigative tool, tractography must provide a principled and accurate portrait of neural connectivity in a majority of realistic situations. Complex fiber architecture is a ubiquitous feature of neuroanatomy, therefore a tractography method that can reliably examine the organization of neural tissue would be of great value. MRI tractography as initiated within the framework of diffusion tensor imaging (DTI) falls short of this goal (Basser, P. J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A., 2000. In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44, 625-632). The tensor model on which it depends is unable to resolve multiple fiber orientations within an MRI voxel, and accordingly cannot resolve fiber crossings either as tract intersections in white matter or in the intricate architecture of gray matter (Mori, S., van Zijl, P. C., 2002. Fiber tracking: principles and strategies—a technical review. NMR Biomed. 15, 468-480). Methods to address this limitation within the DTI framework have been proposed (e.g. Behrens, T. E., Woolrich, M. W., Jenkinson, M., Johansen-Berg, H., Nunes, R. G., Clare, S., Matthews, P. M., Brady, J. M., Smith, S. M., 2003. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50, 1077-1088) but do not suggest a principled, direct and detailed imaging of complex fiber architecture.
Prompted by these limitations, MRI methods have been described that have the capacity to resolve heterogeneity of fiber orientations in each resolved volume of tissue (voxel) (Tuch, D. S., Reese, T. G., Wiegell, M. R., Makris, N., Belliveau, J. W., Wedeen, V. J., 2002. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn. Reson. Med. 48, 577-582), and provide new insights into the organization of cerebral white matter tracts (Schmahmann, J. D., Pandya, D. N., Wang, R., Dai, G., D'Arceuil, H. E., de Crespigny, A. J., Wedeen, V. J., 2007. Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and auto-radiography. Brain 130, 630-653). These methods move beyond DTI. They describe diffusion in each voxel with a general function, the probability density function (PDF) which for each voxel specifies the 3D distribution of microscopic displacements of MR-visible spins that it contains. These methods include diffusion spectrum MRI (DSI) (Lin, C. P., Wedeen, V. J., Chen, J. H., Yao, C., Tseng, W. Y., 2003. Validation of diffusion spectrum magnetic resonance imaging with manganese-enhanced rat optic tracts and ex vivo phantoms. NeuroImage 19, 482-495) in which the PDF is imaged in full with 3D Fourier encoding of displacements, as well as Q-ball methods sensitive predominantly to the angular part of the PDF (Tuch, D. S., Reese, T. G., Wiegell, M. R., Wedeen, V. J., 2003. Diffusion MRI of complex neural architecture. Neuron 40, 885-895) and related approaches (Jansons, K., Alexander, D., 2003. Persistent angular structure: new insights from diffusion magnetic resonance imaging data. Inverse Problems 19, 1031-1046).
Diffusion-weighted magnetic resonance imaging (DWI) has become an established tool in clinical and research settings since a basic diffusion sequence was described by Stejskal and Tanner (Stejskal E O, Tanner J E. Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient. J. Chem. Phys. 1965; 42(1):288-92). Today, single-shot echo-planar imaging (EPI) is the method of choice for most applications unless very high spatial resolution or very small spatial distortions are required in which case multi-shot methods (Pipe J G, Farthing V G, Forbes K P. Multishot diffusion-weighted FSE using PROPELLER MRI. Magn Reson Med. January; 2002 47(1):42-52) are preferred. Diffusion imaging is sensitive to several artifacts (Le Bihan D, Poupon C, Amadon A, Lethimonnier F. Artifacts and pitfalls in diffusion MRI. J Magn Reson Imaging. September; 2006 24(3):478-88). Some of these artifacts are scanner hardware related like distortions resulting from eddy currents and some artifacts are acquisition related like geometric distortions induced by magnetic susceptibility differences across brain tissue when using an echo planar imaging (EPI) readout. Other artifacts are subject related like physiological motion (cardiac pulsation, respiration) and bulk motion. Left uncorrected, slow bulk motion during the acquisition leads to a mismatch of image data. This causes edge artifacts and blurring in the image. In addition, fast bulk motion can lead to inhomogeneous signal attenuation artifacts (signal dropouts) in the diffusion-weighted images caused by additional phase terms (Anderson A W, Gore J C. Analysis and correction of motion artifacts in diffusion weighted imaging. Magn Reson Med. September; 1994 32(3):379-87). Artifacts due to physiological motion are usually minor and can be mitigated by gating, however, bulk motion artifacts often result in unusable images when left uncorrected. While slow bulk motion can be corrected for retrospectively, real-time motion correction reduces motion-related effects and decreases the variance between volumes compared to retrospective motion correction. If the spatial resolution is anisotropic, as is often the case for clinical protocols, interpolation will also result in sub-optimal image quality. Motion can also be corrected online with external motion tracking e.g. using optical methods (Zaitsev M, Dold C, Sakas G, Hennig J, Speck O. Magnetic resonance imaging of freely moving objects: prospective real-time motion correction using an external optical motion tracking system. Neuroimage. July 1; 2006 31(3):1038-50; Dold C, Zaitsev M, Speck O, Firle E A, Hennig J, Sakas G. Advantages and limitations of prospective head motion compensation for MRI using an optical motion tracking device. Acad Radiol. September; 2006 13(9):1093-103; Zaremba A A, MacFarlane D L, Tseng W C, Stark A J, Briggs R W, Gopinath K S, Cheshkov S, White K D. Optical head tracking for functional magnetic resonance imaging using structured light. J Opt Soc Am A Opt Image Sci Vis. July; 2008 25(7):1551-7). However, these methods require additional external hardware and software as well as time-consuming calibration steps before they can be used.
Diffusion images with signal attenuation artifacts cannot easily be corrected retrospectively. To prevent errors in the derived data, these images need to be excluded from processing, leading to a reduction in signal-to-noise ratio (SNR) of the derived maps and a bias in the tensor calculation. To address these problems, reacquisition methods can be used where segments or images that are affected by motion artifacts are reacquired during the same acquisition (Porter D A, Heidemann R M. High resolution diffusion-weighted imaging using readout-segmented echo-planar imaging, parallel imaging and a two-dimensional navigator-based reacquisition. Magn Reson Med. August; 2009 62(2):468-75; Benner, T.; van der Kouwe, A J.; Sorensen, A G. Diffusion imaging with prospective motion correction and reacquisition. Magn Reson Med. 2011 July; 66(1): 154-167).